Biomarkers as Biomedical Bioindicators: Approaches and Techniques for the Detection, Analysis, and Validation of Novel Biomarkers of Diseases

Biosensing of single-nucleotide polymorphism: Technological advances and their transformative applications on health

Single nucleotide polymorphisms (SNPs) are important genetic changes related to many diseases such as breast cancer, Alzheimer's disease, and β-thalassemia. Because of the increased interest in biosensor technologies, there has been a notable surge in the creation of new techniques to identify these changes in recent years. These new methods are highly accurate and sensitive, cost-effective and fast, making them ideal for use in clinical analysis. The non-invasive nature of biosensing techniques further enhances their integration into clinical protocols and point-of-care diagnostics. Several electrochemical, optical, and mass-based biosensors are carefully examined in this extensive review; each is distinguished by unique sensing platforms and techniques. This review presents in-depth discussions of linear dynamic ranges, detection limits, and real-world applications of contemporary research in the diagnosis of biological substrate disorders.

Ocular surface microbiome: Influences of physiological, environmental, and lifestyle factors

PURPOSE

The ocular surface (OS) microbiome is influenced by various factors and impacts on ocular health. Understanding its composition and dynamics is crucial for developing targeted interventions for ocular diseases. This study aims to identify host variables, including physiological, environmental, and lifestyle (PEL) factors, that influence the ocular microbiome composition and establish valid associations between the ocular microbiome and health outcomes.

METHODS

The 16S rRNA gene sequencing was performed on OS samples collected from 135 healthy individuals using eSwab. DNA was extracted, libraries prepared, and PCR products purified and analyzed. PEL confounding factors were identified, and a cross-validation strategy using various bioinformatics methods including Machine learning was used to identify features that classify microbial profiles.

RESULTS

Nationality, allergy, sport practice, and eyeglasses usage are significant PEL confounding factors influencing the eye microbiome. Alpha-diversity analysis revealed significant differences between Spanish and Italian subjects (p-value < 0.001), with a median Shannon index of 1.05 for Spanish subjects and 0.59 for Italian subjects. Additionally, 8 microbial genera were significantly associated with eyeglass usage. Beta-diversity analysis indicated significant differences in microbial community composition based on nationality, age, sport, and eyeglasses usage. Differential abundance analysis identified several microbial genera associated with these PEL factors. The Support Vector Machine (SVM) model for Nationality achieved an accuracy of 100%, with an AUC-ROC score of 1.0, indicating excellent performance in classifying microbial profiles.

CONCLUSION

This study underscores the importance of considering PEL factors when studying the ocular microbiome. Our findings highlight the complex interplay between environmental, lifestyle, and demographic factors in shaping the OS microbiome. Future research should further explore these interactions to develop personalized approaches for managing ocular health.

Breaking the code: Using the Precision Health Model to guide research and clinical care

BACKGROUND

Precision health is a person-centered approach to health and well-being that is operationalized through evaluating omics-level profiles and their associations with the exposome. A precision health approach addresses the challenge that "one size does not fit all" in the management of an individual's health.

PURPOSE

The purpose of this white paper is to introduce a Precision Health Model and its application in research and clinical care.

METHODS

An expert panel reviewed and synthesized the extant literature related to precision health, the current state of omics' science, and common exposome factors that influence the health/illness continuum. A case study provides the framework for the application of the Precision Health Model.

DISCUSSION

Precision health and key domains are defined and serve as the platform for the development of the Precision Health Model.

CONCLUSION

Application of the Precision Health Model will provide inclusive, equitable, person-centered research and clinical care.

Portable Point-of-Care Diagnosis Platforms and Emerging Predictive Biomarkers for Rapid Detection of Severe Dengue Viral Infection

Dengue virus (DENV) infection is a major global public health problem, particularly in tropical and subtropical regions where Aedes mosquitoes are prevalent. The clinical spectrum of dengue ranges from mild febrile illness to severe conditions such as dengue hemorrhagic fever and dengue shock syndrome. Early prediction of dengue progress is crucial for timely therapeutic medications, which can reduce both morbidity and mortality. Traditional diagnostic methods such as serological tests and polymerase chain reactions are often time-consuming and require sophisticated infrastructure and skilled personnel. To overcome these limitations, the development of point-of-care (POC) diagnosis platforms and novel predictive biomarkers is crucial to providing rapid, real-time diagnostic tools that can be used in low-resource settings and at the patient's bedside. Predictive biomarkers enable the identification of disease risk in the early stages and can reduce hospitalization visits. This review offers a comprehensive overview of portable POC diagnosis platforms and emerging predictive biomarkers for the rapid diagnosis of severe DENV infection. Its provides an overview of its epidemiology, discusses the global burden of DENV, and explores DENV infection with different serotypes, as well as the clinical spectrum and severity of dengue. The key focus is on the latest advancements in POC diagnosis readout methods and portable POC devices for DENV diagnosis, including colorimetric assay, electrochemical method, lateral flow strip, and microfluidic chip platforms. In addition, the review article explores various emerging predictive biomarkers for the rapid detection of DENV, while also highlighting the limitations associated with protein, nucleic acid, and metabolic biomarkers. Finally, we address the current challenges, limitations, and potential future directions of POC diagnosis platforms for the diagnosis of severe DENV infection.

The MNK-SYNGAP1 axis in specific learning disorder: gene expression pattern and new perspectives

Specific learning disorder (SLD) is a neurodevelopmental disorder that significantly affects children's academic performance. This study aimed to investigate the expression levels of the MAP Kinase Interacting Serine/Threonine Kinase 1-2 (MNK1, MNK2), Synaptic Ras GTPase Activating Protein 1 (SYNGAP1) genes, and the long non-coding RNA Synaptic Ras GTPase Activating Protein 1-Anti Sense1 (SYNGAP1-AS1), which are believed to play a key role in neurodevelopmental pathways, in children with SLD. Understanding the role of these genes in synaptic plasticity and cognitive function may provide insights into the molecular mechanisms underlying SLD. This study included 38 children diagnosed with SLD and 35 healthy controls aged 6 to 16. RNA was isolated from blood samples, and gene expression levels were measured using quantitative polymerase chain reaction (qPCR). The statistical analysis was conducted to compare the expression levels between the SLD and control groups and within SLD subgroups based on severity and sex. MNK1 and SYNGAP1 expression levels were significantly upregulated in the SLD group compared to the control group (8.33-fold and 16.52-fold increase, respectively; p < 0.001). lncSYNGAP1-AS1 showed a 26.58-fold increase, while MNK2 was downregulated by 2.2-fold, although these changes were not statistically significant. No significant differences were observed between sexes or between the severity subgroups of SLD.

CONCLUSION

he upregulation of MNK1 and SYNGAP1 in children with SLD suggests their involvement in the neurodevelopmental pathways associated with cognitive processes such as learning and memory. These findings provide a foundation for future research into the molecular basis and potential therapeutic targets of SLD.

WHAT IS KNOWN

• SYNGAP1 is a key regulator of synaptic plasticity and learning, primarily functioning through Ras signaling inhibition. Its deficiency impairs long-term potentiation (LTP) and is associated with neurodevelopmental disorders (NDDs) such as autism spectrum disorder (ASD) and intellectual disability. • The MAPK/ERK pathway plays a crucial role in learning and memory, and its dysregulation has been linked to several neurological conditions. MNK1/2 interacts with SYNGAP1 in synaptic signaling.

WHAT IS NEW

• This study is the first to demonstrate significant upregulation of SYNGAP1 and MKNK1 in children with SLD. • Understanding the role of the MKNK-SYNGAP1 axis may guide the development of targeted therapies aimed at enhancing synaptic plasticity to improve learning and memory outcomes in children with SLD.

Evaluating Cryopreservation Methods in Biobanking: Impacts on Biomarker Integrity and Omics Data Reliability

Objectives: Personalized medicine emphasizes prevention and early diagnosis by developing genetic screening and biomarker assessment tools. Biobanks, including University of Piemonte Orientale (UPO) Biobank, support this effort by providing high-quality biological samples collected, processed, and stored using optimized standardized protocols. To determine the optimal long-term storage conditions for biospecimens used in biomedical research, we evaluated plasma and serum samples cryopreserved using two storage methods, cryovials and straws, across various analytical methodologies with differing sensitivity and robustness. Design and Methods: Plasma and serum samples cryopreserved in liquid nitrogen in vials and straw at the UPO Biobank were subjected to multiple analyses including standard biochemical laboratory analysis, targeted lipidomics, untargeted proteomics, and targeted metabolites quantification through mass spectrometry-based analytical techniques. Results: Our data demonstrate the robustness and applicability of both storage methods for standard laboratory analyses in evaluating clinically relevant markers in plasma and serum. Lipidomic analysis revealed slight disparities in lipid abundance, though these differences were mostly confined to specific lipid species, particularly fatty acids. Conversely, proteomic and metabolomic analyses uncovered variations in abundance in a significant, albeit limited, fraction of analytes between vials and straw-derived samples. Conclusions: By highlighting similarities and differences in samples stored in these conditions, this study provides significant insights into optimizing biobanking practices and understanding the factors that influence the integrity of cryopreserved biospecimens and the reliability of the data derived from them. Both straws and vials are convenient and efficient cryopreservation methods, essentially equivalent for samples dedicated to robust and relatively low-sensitive standardized analyses. However, our findings emphasize the need for caution when interpreting omics data from samples subjected to different cryopreservation methods, as subtle variations can arise even with different types of containers.

Analysis of Personalized Cardiovascular Drug Therapy: From Monitoring Technologies to Data Integration and Future Perspectives

Cardiovascular diseases have long been a major challenge to human health, and the treatment differences caused by individual variability remain unresolved. In recent years, personalized cardiovascular drug therapy has attracted widespread attention. This paper reviews the strategies for achieving personalized cardiovascular drug therapy through traditional dynamic monitoring and multidimensional data integration and analysis. It focuses on key technologies for dynamic monitoring, dynamic monitoring based on individual differences, and multidimensional data integration and analysis. By systematically reviewing the relevant literature, the main challenges in current research and the proposed potential directions for future studies were summarized.

Blood Biomarkers in Ischemic Stroke Diagnostics and Treatment-Future Perspectives

Stroke is a leading cause of disability and the second most common cause of death worldwide, with its incidence increasing due to an aging population. Early diagnosis is crucial for timely medical intervention. Biomarkers serve as objective indicators to predict outcomes, monitor treatment responses, and assess prognosis. This review examines the evolving landscape of stroke biomarkers, highlighting their potential clinical applications and the challenges hindering their widespread use. Blood biomarkers are readily accessible and provide insight into the pathophysiological processes underlying stroke. This review focuses on neuronal and glial biomarkers, as well as those associated with inflammation, thrombosis, excitotoxicity, and neuroprotection. Also, it focuses on genetic biomarkers. The timing of biomarker measurement is particularly critical in the early stages of stroke, when rapid decision-making is essential, and it requires systematic investigation. Although numerous molecules have been proposed as stroke biomarkers in recent years, none have yet been integrated into routine clinical practice. Stroke biomarkers hold great promise for enhancing diagnosis, risk stratification, and personalized treatment strategies. However, well-designed studies and rigorous validation are necessary to bridge the gap between research findings and clinical implementation. Integrating biomarkers with existing diagnostic tools could revolutionize stroke management and improve patient outcomes. Continued research into blood biomarkers and their clinical utility remains imperative for advancing stroke care.

MicroRNAs in lung cancer: their role in tumor progression, biomarkers, diagnostic, prognostic, and therapeutic relevance

MicroRNAs (miRNAs) are a class of small non-coding RNAs which are associated with post-transcriptional regulation of gene expression. Dysfunction or aberrant expression of miRNAs is predominant in various malignancies including lung cancer. Lung cancer is one of the commonest causes of cancer-related death worldwide, with a five-year survival of only 10-20%. The present review summarizes the current understanding of the role of miRNAs in the development and progression of human lung cancer and their therapeutic potential. Also, we briefly discuss the canonical biogenetic pathway of miRNAs followed by a detailed illustration on how miRNAs regulate human lung cancer progression in various ways. Furthermore, we focus on how miRNAs contribute to the crosstalk between cancer cells and different cells in the tumor microenvironment in the context of lung cancer. Finally, we illustrate how different miRNAs are used as a prognostic and diagnostic biomarker for lung cancer and the ongoing miRNA-associated clinical trials. In conclusion, we discuss how targeting miRNAs can be a potential therapeutic means in the treatment of human lung cancer.

Platelet-Rich Plasma and Electrochemical Biosensors: A Novel Approach to Ovarian Function Evaluation and Diagnostics

Preserving ovarian function is important to women's reproductive health. It is necessary for fertility and maintaining the overall hormonal balance. Platelet-rich plasma (PRP) is an autologous plasma containing a predominately platelet concentrate prepared from fresh blood. It has been observed that PRP injections into the ovary can renew the functional cells of the cortical layer of the ovary follicles and reactivate the production of sex hormones. It may improve a woman's fertility in the case of premature ovarian failure, the condition after chemotherapy treatment, or during the climacteric period. The main markers to evaluate the procedure's success are elevated anti-Müllerin hormone and enlarged count level of atrial follicles in ovaries. The aim of this review is to identify the ovarian PRP procedure success markers and point out the electrochemical sensor techniques. Literature was selected depending on including and excluding criteria; studies were sorted by topics in two blocks: PRP biomarkers and electrochemistry. As PRP acts as a regenerative care, electrochemical biosensors can provide accurate, real-time data to evaluate the biological response to PRP therapy. The biosensors' ability to monitor hormonal levels and follicle development serves as objective markers of the effectiveness of PRP in restoring ovarian function. Together, these approaches enable a more precise evaluation of ovarian health and fertility outcomes after PRP intervention.

Comprehensive analysis of intervention and control studies for the computational identification of dengue biomarker genes

Dengue fever, caused by the dengue virus (DENV), presents a significant global health concern, with millions of cases reported annually. Despite significant progress in understanding Dengue fever, effective prognosis and treatment remain elusive due to the complex clinical presentations and limitations in current diagnostic methods. The virus, transmitted primarily by the Aedes aegypti mosquito, exists in four closely related forms, each capable of causing flu-like symptoms ranging from mild febrile illness to severe manifestations such as plasma leakage and hemorrhagic fever. Although advancements in diagnostic techniques have been made, early detection of severe dengue remains difficult due to the complexity of its clinical presentations. This study conducted a comprehensive analysis of differential gene expression in dengue fever patients using multiple microarray datasets from the NCBI GEO database. Through bioinformatics approaches, 163 potential biomarker genes were identified, with some overlapping previously reported biomarkers and others representing novel candidates. Notably, AURKA, BUB1, BUB1B, BUB3, CCNA2, CCNB2, CDC6, CDK1, CENPE, EXO1, NEK2, ZWINT, and STAT1 were among the most significant biomarkers. These genes are involved in critical cellular processes, such as cell cycle regulation and mitotic checkpoint control, which are essential for immune cell function and response. Functional enrichment analysis revealed that the dysregulated genes were predominantly associated with immune response to the virus, cell division, and RNA processing. Key regulatory genes such as AURKA, BUB1, BUB3, and CDK1 are found to be involved in cell cycle regulation and have roles in immune-related pathways, underscoring their importance in the host immune response to Dengue virus infection. This study provides novel insights into the molecular mechanisms underlying Dengue fever pathogenesis, highlighting key regulatory genes such as AURKA and CDK1 that could serve as potential biomarkers for early diagnosis and targets for therapeutic intervention, paving the way for improved management of the disease.

Recent Advances in Aptamers-Based Nanosystems for Diagnosis and Therapy of Cardiovascular Diseases: An Updated Review

The increasing global prevalence of cardiovascular diseases highlights the urgent need for innovative diagnostic and therapeutic strategies. Aptamers, small single-stranded nucleic acid molecules with exceptional specificity and affinity for target biomolecules, have emerged as promising tools for precise diagnostics and targeted therapies. Their selective binding capabilities provide valuable insights into the molecular mechanisms underlying cardiovascular conditions. When integrated into nanosystems, aptamers enhance the delivery, bioavailability, and stability of diagnostic and therapeutic agents, addressing challenges of solubility and degradation. This integration enables more targeted drug delivery, advanced imaging techniques, and improved therapeutic interventions, ultimately improving the management of cardiovascular diseases. Recent advancements in aptamer selection methodologies, coupled with their unique three-dimensional structures, have significantly expanded their application potential in cardiovascular health. By combining aptamers with nanosystems, novel approaches to cardiovascular disease diagnosis and treatment are emerging, promising enhanced efficacy, safety, and precision. This review explores recent progress in the development and application of aptamer-based nanosystems in cardiovascular diagnostics and therapies.

Advances in Wearable Biosensors for Wound Healing and Infection Monitoring

Wound healing is a complicated biological process that is important for restoring tissue integrity and function after injury. Infection, usually due to bacterial colonization, significantly complicates this process by hindering the course of healing and enhancing the chances of systemic complications. Recent advances in wearable biosensors have transformed wound care by making real-time monitoring of biomarkers such as pH, temperature, moisture, and infection-related metabolites like trimethylamine and uric acid. This review focuses on recent advances in biosensor technologies designed for wound management. Novel sensor architectures, such as flexible and stretchable electronics, colorimetric patches, and electrochemical platforms, enable the non-invasive detection of changes associated with wounds with high specificity and sensitivity. These are increasingly combined with AI and analytics based on smartphones that can enable timely and personalized interventions. Examples are the PETAL patch sensor that applies multiple sensing mechanisms for wide-ranging views on wound status and closed-loop systems that connect biosensors to therapeutic devices to automate infection control. Additionally, self-powered biosensors that tap into body heat or energy from the biofluids themselves avoid any external batteries and are thus more effective in field use or with limited resources. Internet of Things connectivity allows further support for remote sharing and monitoring of data, thus supporting telemedicine applications. Although wearable biosensors have developed relatively rapidly and their prospects continue to expand, regular clinical application is stalled by significant challenges such as regulatory, cost, patient compliance, and technical problems related to sensor accuracy, biofouling, and power, among others, that need to be addressed by innovative solutions. The goal of this review is to synthesize current trends, challenges, and future directions in wound healing and infection monitoring, with emphasis on the potential for wearable biosensors to improve patient outcomes and reduce healthcare burdens. These innovations are leading the way toward next-generation wound care by bridging advanced materials science, biotechnology, and digital health.

Amplification-free detection of Mycobacterium tuberculosis using CRISPR-Cas12a and graphene field-effect transistors

Current molecular tests for tuberculosis (TB), such as whole genome sequencing and Xpert Mycobacterium tuberculosis/rifampicin resistance assay, exhibit limited sensitivity and necessitate the pre-amplification step of target DNA. This limitation greatly increases detection time and poses an increased risk of infection. Here, we present a graphene field-effect transistor (GFET) based on the CRISPR/Cas system for detecting Mycobacterium tuberculosis. The CRISPR/Cas12a system has the ability to specifically recognize and cleave target DNA. By integrating the system onto the FET platform and utilizing its electrical amplification capability, we achieve rapid and sensitive detection without requiring sample pre-amplification, with a limit of detection (LoD) as low as 2.42 × 10-18 M. Cas12a-GFET devices can differentiate 30 positive cases from 56 serum samples within 5 minutes. These findings highlight its immense potential in future biological analysis and clinical diagnosis.

Introducing AVAC as an ultra-sensitive platform with broad dynamical range for high-throughput multiplexed biomarker detection using digital counting of plasmonic nanoparticles

Accurate detection and quantification of biomarkers at ultra-low levels is critical for disease diagnosis and effective treatment. Traditional detection technologies often lack the sensitivity, specificity, throughput, or multiplexing capacity required for comprehensive diagnostics, providing only a subset of these requirements. Here, we introduce AVAC, an automated optical technology for rapid and accurate biomarker detection with ultra-high sensitivity that significantly outperforms standard clinical assays. The core of this technology is the digital counting of plasmonic nanoparticles used as optical labels, enabling multiplexed, high-throughput detection of biomarkers. Validation studies demonstrate AVAC's high accuracy, with 98.2% specificity and detection limits as low as 26 fg/mL for HIV p24 protein and a quantification range of 160 fg/mL to 850 pg/mL for interleukin-6 (IL-6). The technology supports multiplexed assays without compromising sensitivity, as demonstrated by the simultaneous detection of three key biomarkers associated with cardiovascular disease. A counting range spanning more than four orders of magnitude ensures robust detection from ultra-low to high biomarker concentrations, and its ability to analyze up to 1,000 samples per hour provides high throughput suitable for large laboratories. With its unique combination of capabilities, this versatile platform has significant potential to advance biomarker-based diagnostics in clinical and research settings.

A review on pyrimidine-based pharmacophore as a template for the development of hybrid drugs with anticancer potential

The low efficacy and toxicity of traditional chemotherapy, led by drug resistance of targeted anticancer therapies, have mandated the exploration and development of anticancer molecules. In this league, hybrid drugs, owing to their peculiar multitargeted functionality and structural diversity, could serve as vital leads in this quest for drug discovery. They are plausibly found to offer added advantages considering the improved efficacy, low toxicity, and improved patient compliance. Among numerous heterocycles explored, pyrimidine derivatives epitomize as a valuable resource for the hybrid drug development due to their validated efficacy and versatility. The present review discusses the role of pyrimidine, a diversified pharmacophore in drug development and concepts of hybrid drugs. The study covers the recent advancements in pyrimidine-based hybrid pharmacophores. It delves further into the challenges in hybrid drug development and ongoing research in hybrid drug discovery. Furthermore, the challenges faced in developing hybrid molecules, such as their design and optimization complexities, bioavailability and pharmacokinetics issues, target identification and validation, and off-target effects, are discussed.

Innovative laboratory techniques shaping cancer diagnosis and treatment in developing countries

Cancer is a major global health challenge, with approximately 19.3 million new cases and 10 million deaths estimated by 2020. Laboratory advancements in cancer detection have transformed diagnostic capabilities, particularly through the use of biomarkers that play crucial roles in risk assessment, therapy selection, and disease monitoring. Tumor histology, single-cell technology, flow cytometry, molecular imaging, liquid biopsy, immunoassays, and molecular diagnostics have emerged as pivotal tools for cancer detection. The integration of artificial intelligence, particularly deep learning and convolutional neural networks, has enhanced the diagnostic accuracy and data analysis capabilities. However, developing countries face significant challenges including financial constraints, inadequate healthcare infrastructure, and limited access to advanced diagnostic technologies. The impact of COVID-19 has further complicated cancer management in resource-limited settings. Future research should focus on precision medicine and early cancer diagnosis through sophisticated laboratory techniques to improve prognosis and health outcomes. This review examines the evolving landscape of cancer detection, focusing on laboratory research breakthroughs and limitations in developing countries, while providing recommendations for advancing tumor diagnostics in resource-constrained environments.

Predicting interactome networks of up/down regulated proteins and drug-gene interaction analysis associated with peri-implantitis

Background

Peri-implantitis is implant-associated inflammation that leads to irreversible loss of surrounding bone. Early diagnosis increases the success of peri-implantitis treatment. Despite various studies associated with this most common complication, early detection of the onset of peri-implantitis remains a major challenge. Molecular biomarkers are applicable detectors for the early detection of numerous diseases and monitoring their development. The present study aimed to predict interactome networks of up/down regulated proteins and analyze drug-gene interaction in peri-implantitis to identify the diagnostic and druggable genes.

Materials and Methods

In this in silico study, a suitable gene expression profile related to peri-implantitis was retrieved from Gene Expression Omnibus. Screening differentially expressed genes (DEGs) was carried out based on the cut-off criteria |log2 (fold change)|>2 and P < 0.05. Interactome networks were constructed and analyzed by the STRING database (Version: 12.0) and the Cytoscape software (version: 3.9.1). Finally, to investigate drug-gene interaction, detected hub genes were analyzed by DGIdb (version: 5.0.6).

Results

A total of 216 genes were identified as DEGs (129 down-regulated and 87 up-regulated genes) in peri-implantitis. Regarding Cytoscape analysis, FCGR3B, CSF3R, AQP9, TREM1, and P2RY13 were the top 5 hub nodes of up-regulated DEGs, and CXCL10, OASL, IFIT1, RSAD2, and ISG15 were the top 5 hub nodes of down-regulated DEGs. Among these key nods, AQP9, CSF3R, CXCL10, IFIT1, ISG15, OASL, and, FCGR3B were therapeutic targets and had approved drugs.

Conclusion

In this research, seven genes have been identified as druggable genes in peri-implantitis which can be used to treat and diagnose this disease. However, these results and identified genes need to be validated by clinical or experimental methods.

Bridging the gap: From petri dish to patient - Advancements in translational drug discovery

Translational research serves as the bridge between basic research and practical applications in clinical settings. The journey from "bench to bedside" is fraught with challenges and complexities such as the often-observed disparity between how compounds behave in a laboratory setting versus in the complex systems of living organisms. The challenge is further compounded by the limited ability of in vitro models to mimic the specific biochemical environment of human tissues. This article explores and details the recent advancements and innovative approaches that are increasingly successful in bridging the gap between laboratory research and patient care. These advancements include, but are not limited to, sophisticated in vitro models such as organ-on-a-chip and computational models that utilize artificial intelligence to predict drug efficacy and safety. The article aims to showcase how these technologies improve the predictability of drug performance in human bodies and significantly speed up the drug development process. Furthermore, it discusses the role of biomarker discovery in preparation of more targeted and personalized therapy approaches and covers the impact of regulatory changes designed to facilitate drug approvals. Additionally, by providing detailed case studies of successful applications, we illustrate the practical impacts of these innovations on drug discovery and patient care.

Efficiency of electrochemical immuno- vs. apta(geno)sensors for multiple cancer biomarkers detection

The interest in biosensors technology has been constantly growing over the last few years. It is still the biggest challenge to design biosensors able to detect two or more analytes in a single measurement. Electrochemical methods are frequently used for this purpose, mainly due to the possibility of applying two or more different redox labels characterized by independent and distinguished electrochemical signals. In addition to antibodies, nucleic acids (aptamers) have been increasingly used as bioreceptors in the construction of such sensors. Within this review paper, we have collected the examples of electrochemical immuno- and geno(apta)sensors for simultaneous detection of multiple analytes. Based on many published literature examples, we have emphasized the recent application of multiplexed platforms for detection of cancer biomarkers. It has allowed us to compare the progress in design strategies, including novel nanomaterials and amplification of signals, to get as low as possible limits of detection. We have focused on multi-electrode and multi-label strategies based on redox-active labels, such as ferrocene, anthraquinone, methylene blue, thionine, hemin and quantum dots, or metal ions such as Ag+, Pb2+, Cd2+, Zn2+, Cu2+ and others. We have finally discussed the possible way of development, challenges and prospects in the area of multianalyte electrochemical immuno- and geno(apta)sensors.

Identification of significant hub genes and pathways associated with metastatic breast cancer and tolerogenic dendritic cell via bioinformatics analysis

Metastatic breast cancer (MBC) is an advanced-stage breast cancer associated with more than 90 % of cancer-related deaths. Immunosuppressive properties of tolerogenic dendritic cells (tolDCs) in tumour immune microenvironment (TIME) may be a risk factor for the rapid progression to MBC. However, the exact connections between the two are unknown. The aim of the current study is to uncover gene signatures and key pathways associated with MBC and tolDCs via an integrated bioinformatics approach. Gene expression profiles of MBC and tolDCs were retrieved from Gene Expression Omnibus (GEO) to identify common differentially expressed genes (DEGs). From DGE analysis, 529 upregulated common DEGs and 367 downregulated common DEGs had been identified. In enrichment analysis, common DEGs enriched in GO terms of defense response to virus and KEGG pathway of transcriptional misregulation in cancer were reported to be significantly associated with MBC and tolDCs. From the constructed PPI networks, 23 hub genes were identified, although only 5 genes were significant; 3 upregulated (ISG15, OAS2 and RSAD2) and 2 downregulated (eEF2 and PPARG) as they were found to be significantly correlated and had the same expression trend as predicted in validation analysis of overall survival (OS) analysis, expression levels, immune infiltration analysis and immunohistochemistry (IHC) analysis. These 5 hub genes can now be exploited in developing novel therapeutic interventions and as diagnostic biomarkers for enhancing the clinical outcomes of MBC patients.

The Role of Oral Biomarkers in the Assessment of Noncommunicable Diseases

Background/Objectives: Oral biomarkers have gained attention as non-invasive tools for assessing systemic diseases due to their potential to reflect physiological and pathological conditions. This review aims to explore the role of oral biomarkers in diagnosing and monitoring systemic diseases, emphasizing their diagnostic relevance and predictive capabilities in clinical practice. Methods: This narrative review synthesizes the current literature on biochemical, immunological, genetic, and microbiological oral biomarkers, with a focus on their sources, types, and clinical applications. Key studies were analyzed to identify associations between oral biomarkers and systemic diseases such as cardiovascular diseases, type 2 diabetes mellitus, autoimmune disorders, and cancers. Results: Oral fluids, including saliva and gingival crevicular fluid, contain diverse biomarkers such as matrix metalloproteinases, cytokines, and genetic indicators. These markers have demonstrated potential in diagnosing and monitoring systemic conditions. Among others, elevated levels of salivary glucose and inflammatory cytokines correlate with diabetes progression, while vascular endothelial growth factor (VEGF) and salivary C-reactive protein might be applicable as indicators for periodontal disease and cardiovascular risk. Additionally, salivary biomarkers like amyloid-beta and tau are promising in detecting neurodegenerative disorders. Conclusions: Oral biomarkers might represent a transformative and point-of-care approach to the early management of systemic diseases; however, challenges in measurement variability, standardization, and validation remain.

Behavioral and biomedical factors associated with lifestyle modification practices among diagnosed hypertensive patients in pastoral health facilities of southern Ethiopia

Background

More than 23 million deaths and 36.5% of disability-adjusted life-years are the result of the direct effects of unhealthy behavior alone. Daily behaviors have strong implications for health outcomes and quality of life. The aim of this study is to determine the behavioral and biomedical factors associated with lifestyle modification practices among diagnosed hypertensive patients in pastoral health facilities of southern Ethiopia.

Methods

A facility-based cross-sectional study was conducted among 453 diagnosed hypertensive adult patients in pastoral health of southern Ethiopia from June 1/2023 to July 30/2023. The study population was randomly selected from among patients diagnosed with hypertension that was followed up during the study period using a systematic random sampling technique. The data were entered into Epi-Data-4.6.0.2 and exported to SATAT version 14 for analysis. A binary logistic regression model was fitted to determine independent predictors of lifestyle modification practices among hypertensive patients. An adjusted odds ratio with a 95% confidence interval was used to declare a state of significance.

Results

Out of 453 potential participants approached, 433 agreed to successfully participate in the study, for a response rate of 95.6%. Of the total participants, 56.1% (95% CI, 51.38-60.74) of the patients practiced the recommended lifestyle modifications. Alcohol consumption (AOR = 0.64, 95% CI: 0.42-0.96), ever-practiced reducing salt intake (AOR = 2.48, 95% CI: 1.57-3.93), and low-density lipoprotein cholesterol levels in the blood (>160 mg/dl) (AOR = 3.3, 95% CI: 1.72-6.34) were independently associated with lifestyle modifications in patients with hypertension.

Conclusion

This study revealed that the prevalence of lifestyle modification practices (LMP) was low among hypertensive patients. Lifestyle modification is not one-stop practical, but continuous proper awareness creation, counseling, and health education and health promotion are needed to scale up healthy behavior in patients with hypertension to create a good lifestyle.

Effect of Arsenic Exposure on AS3MT Protein Levels in Serum of Type 2 Diabetic Patients Compared to Non-diabetics

This research explores the impact of arsenic exposure on serum protein profiles in type 2 diabetes patients, with an emphasis on the AS3MT protein as a biomarker. Utilizing Bradford protein assay, SDS-PAGE, HPLC, and mass spectrometry, we quantified and analyzed variations in serum protein levels, focusing on differences between control groups (82.94 ± 8.03 µg/mL) and diabetic patients (96.95 ± 5.02 µg/mL) of high arsenic exposed in areas Kasur and Lahore, Punjab, Pakistan. The study revealed a significant increase in total serum proteins and specifically identified elevated levels of AS3MT in the diabetic group compared to controls. By using 15% gel, proteins were separated, and bands were visible at 42KD. Further investigations using HPLC provided a detailed chromatographic profile of AS3MT, isolating this protein effectively and displaying its heightened abundance through a marked peak within the sample chromatograms. Additionally, intact mass and tryptic digestion profiles analyzed by mass spectrometry (molecular weight of 41,747.79 D) further corroborated the identity and modification of AS3MT in the context of arsenic exposure. ELISA was used for the quantification of AS3MT protein concentration, and a 260% increase was confirmed in the diabetic group exposed to arsenic. These findings suggested that arsenic exposure significantly alters AS3MT protein and serum protein levels in diabetic patients, supporting the hypothesis that AS3MT can serve as a biomarker for arsenic-induced diabetic conditions.

Dynamic biomarker profiles in patients with paroxysmal atrial fibrillation: Assessing the differences between sinus rhythm and acute atrial fibrillation episode

BACKGROUND

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia in adults, yet its underlying pathophysiology remains poorly understood. This study assessed whether circulating biomarker concentrations differ in paroxysmal AF patients during an acute episode compared to sinus rhythm.

METHODS

The Time of Calamity study is a prospective biomarker study within the RACE V study. Patients underwent venous blood sampling in sinus rhythm at inclusion in RACE V, as well as during a subsequent acute episode of AF for which patients reported to the hospital. Ten biomarkers were analyzed.

RESULTS

Thirty-nine patients (mean age 60 ± 9 years, 10 (25 %) women) were enrolled. During an acute AF episode, dickkopf-related protein 3 and insulin-like growth factor binding protein 7 were significantly lower, while N-terminal pro B-type natriuretic peptide (NT-proBNP), high-sensitive troponin T (hsTnT), growth differentiation factor 15, and interleukin 6 were significantly higher (all p < 0.05).

CONCLUSIONS

Biomarker concentrations in paroxysmal AF patients are dynamic and differ between sinus rhythm and acute AF episodes. Notably, NT-proBNP and hsTnT, commonly used in clinical practice, were significantly elevated during an acute AF episode.

Exploring clinical chemistry markers in amyotrophic lateral sclerosis: insights into survival and disease trajectories

OBJECTIVE

Commonly measured clinical chemistry markers might be indicative of survival and disease progression in amyotrophic lateral sclerosis (ALS).

METHODS

In a cohort study of 270 ALS patients diagnosed from April 2014 to May 2021 in Stockholm, Sweden, we examined the link between 29 clinical chemistry markers at diagnosis and mortality risk at 6 months, 1 year, and 3 years after diagnosis. Summary variables from exploratory factor analysis (EFA) assessed the markers' collective impact on survival. We integrated ALS functional rating scale-revised (ALSFRS-R) scores with survival data using a joint latent class model to identify patterns of functional decline. Multinomial logistic regression determined how the EFA-derived factors predicted the decline trajectories post-diagnosis.

RESULTS

Higher levels of total cholesterol, low-density lipoprotein cholesterol (LDL-C), apolipoprotein B, and albumin at diagnosis were linked to lower mortality in ALS patients, while increased neurofilament light chain (NfL), leukocyte count, mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), and carbon dioxide (CO2) levels indicated higher mortality. The 'Red blood cell profile' factor, derived from EFA, emerged as a significant predictor of survival, independent of other prognostic indicators. The joint latent class model identified three distinct patient groups based on functional decline, with 'Red blood cell profile' suggesting a lower likelihood of being in the groups with slower progression.

CONCLUSION

Clinical chemistry markers, including NfL, lipids, albumin, leukocyte count, MCV, MCH, CO2, and the 'Red blood cell profile,' were associated with ALS survival. As these markers represent broader bodily functions, integrating them in ALS patient care could improve disease management.

Epigenetic biomarkers in Alzheimer's disease: Diagnostic and prognostic relevance

Alzheimer's disease (AD) is a leading cause of cognitive decline in the aging population, presenting a critical need for early diagnosis and effective prognostic tools. Epigenetic modifications, including DNA methylation, histone modifications, and non-coding RNAs, have emerged as promising biomarkers for AD due to their roles in regulating gene expression and potential for reversibility. This review examines the current landscape of epigenetic biomarkers in AD, emphasizing their diagnostic and prognostic relevance. DNA methylation patterns in genes such as APP, PSEN1, and PSEN2 are highlighted for their strong associations with AD pathology. Alterations in DNA methylation at specific CpG sites have been consistently observed in AD patients, suggesting their utility in early detection. Histone modifications, such as acetylation and methylation, also play a crucial role in chromatin remodelling and gene expression regulation in AD. Dysregulated histone acetylation and methylation have been linked to AD progression, making these modifications valuable biomarkers. Non-coding RNAs, including microRNAs (miRNAs) and long non-coding RNAs (lncRNAs), further contribute to the epigenetic regulation in AD. miRNAs can modulate gene expression post-transcriptionally and have been found in altered levels in AD, while lncRNAs can influence chromatin structure and gene expression. The presence of these non-coding RNAs in biofluids like blood and cerebrospinal fluid positions them as accessible and minimally invasive biomarkers. Technological advancements in detecting and quantifying epigenetic modifications have propelled the field forward. Techniques such as next-generation sequencing, bisulfite sequencing, and chromatin immunoprecipitation assays offer high sensitivity and specificity, enabling the detailed analysis of epigenetic changes in clinical samples. These tools are instrumental in translating epigenetic research into clinical practice. This review underscores the potential of epigenetic biomarkers to enhance the early diagnosis and prognosis of AD, paving the way for personalized therapeutic strategies and improved patient outcomes. The integration of these biomarkers into clinical workflows promises to revolutionize AD management, offering hope for better disease monitoring and intervention.

Psoriasis severity assessment: Optimizing diagnostic models with deep learning

Psoriasis is a chronic skin condition with challenges in the accurate assessment of its severity due to subtle differences between severity levels. The aim of this study was to evaluate deep learning models for automated classification of psoriasis severity. A dataset containing 1,546 clinical images was subjected to pre-processing techniques, including cropping and applying noise reduction through median filtering. The dataset was categorized into four severity classes: none, mild, moderate, and severe, based on the Psoriasis Area and Severity Index (PASI). It was split into 1,082 images for training (70%) and 463 images for validation and testing (30%). Five modified deep convolutional neural networks (DCNN) were evaluated, including ResNet50, VGGNet19, MobileNetV3, MnasNet, and EfficientNetB0. The data were validated based on accuracy, precision, sensitivity, specificity, and F1-score, which were weighted to reflect class representation; Pairwise McNemar's test, Cochran's Q test, Cohen's Kappa, and Post-hoc test were performed on the model performance, where overall accuracy and balanced accuracy were determined. Findings revealed that among the five deep learning models, ResNet50 emerged as the optimum model with an accuracy of 92.50% (95%CI: 91.2-93.8%). The precision, sensitivity, specificity, and F1-score of this model were found to be 93.10%, 92.50%, 97.37%, and 92.68%, respectively. In conclusion, ResNet50 has the potential to provide consistent and objective assessments of psoriasis severity, which could aid dermatologists in timely diagnoses and treatment planning. Further clinical validation and model refinement remain required.

Investigation of Association Between Expression of DYX1C1, KIAA0319, and ROBO1 Genes and Specific Learning Disorder in Children and Adolescents

Specific learning disorder (SLD) is prevalent worldwide and is a complex disorder with variable symptoms and significant differences among individuals. Epigenetic markers may alter susceptibility to neurodevelopmental disorders (NDDs). Aberrant expression of protein-coding (mRNA) genes in this pathology shows that the detection of epigenetic molecular biomarkers is of increasing importance in the diagnosis and treatment of individuals with SLD. We compared gene expression level of dyslexia susceptibility 1 candidate gene 1 (DYX1C1), dyslexia-associated protein KIAA0319 (KIAA0319), and roundabout guidance receptor 1 (ROBO1) between children with SLD and healthy children by performing quantitative polymerase chain reaction (qPCR). In addition, we evaluated these gene expressions of severe children with SLD compared to non-severe and male SLD children compared to females. The expression of the DYX1C1, KIAA0319, and ROBO1 genes was statistically significantly upregulated in children with SLD (P < 0.05*). DYX1C1 was also upregulated in severe SLD children (P = 0.03*). In addition, KIAA0319 and ROBO1 genes were differentially expressed in male SLD children compared to females (P < 0.05*). Furthermore, we found that DYX1C1 and ROBO1 genes significantly affect the likelihood of the SLD (respectively, P < 0.001** and P = 0.007*). We expect that the findings provided from this study may contribute to the determination expression level of the relevant genes in the diagnosis, prognosis, and treatment of SLD. In addition, our findings could be a guide for future epigenetics studies on the use of the DYX1C1, KIAA0319, and ROBO1 in therapeutic applications in the SLD.

A Comprehensive Review on Capillary Electrophoresis-Mass Spectrometry in Advancing Biomolecular Research

This review provides an in-depth exploration of capillary electrophoresis-mass spectrometry (CE-MS) in biomolecular research from 2020 to 2024. CE-MS emerges as a versatile and powerful tool due to its numerous advantages, facilitating the analysis of various biomolecules, including proteins, peptides, oligonucleotides, and other metabolites, such as lipids, carbohydrates, or amines, among others. The review extends to various CE modes and interfaces for the CE-MS coupling, offering comprehensive insights into their applications within biomolecular research. Furthermore, it effectively summarizes the conditions employed in CE-MS while also addressing critical aspects such as sample preparation requirements. Despite its advantages, the review highlights a gap between discovery and practical implementation, underscoring the need for large-scale validation and method standardization to fully realize the potential of CE-MS in biomolecular research.

Adrenomedullin as a New Prosperous Biomarker in Infections: Current and Future Perspectives

Adrenomedullin has emerged as a promising biomarker in the field of viral diseases. Numerous studies have demonstrated its potential in assessing disease severity, predicting clinical outcomes, and monitoring treatment response. Adrenomedullin (AM) is a multifaceted peptide implicated in vasodilation, hormone secretion, antimicrobial defense, cellular growth, angiogenesis, and, importantly, chronic pain. AM and related peptides interface with cytoskeletal proteins within neuronal contexts, influencing microtubule dynamics. AM has primarily been utilized in diagnosing diseases of bacterial origin, including sepsis. Nevertheless, there are reports suggesting its utility in diseases of viral origin, and this is the focus of the present study. Furthermore, adrenomedullin has been shown to be elevated in various viral infections, suggesting its role in immune response modulation. Furthermore, AM may contribute to neuronal dysfunction through mechanisms involving immune and inflammatory responses, apoptosis, and disruptions in calcium homeostasis. This review aims to consolidate current knowledge regarding AM and its potential implications in viral diseases, elucidating its diverse roles in neurological pathophysiology. This review highlights the growing importance of adrenomedullin as a biomarker in viral diseases and the need for further functional studies to understand the underlying mechanisms involved.

Targeting Neurological Disorders with Stilbenes: Bridging the Preclinical-Clinical Gap

Neurological disorders (NDs) encompass a range of debilitating conditions that affect the nervous system, including prevalent illnesses such as Alzheimer's disease, Parkinson's disease, and ischemic stroke. Despite significant ongoing studies, effective therapeutic strategies to halt or slow down the progression of these illnesses are still lacking. Stilbenes, a class of natural polyphenols, have shown potential as candidates for therapeutic strategies due to their capacity to protect the nervous system. Preclinical studies have provided strong evidence that stilbenes can regulate many cellular pathways implicated in neurodegeneration, with resveratrol being a well-studied compound that has shown the ability to reduce oxidative damage, promote neurogenesis, and enhance mitochondrial function - crucial for maintaining brain health. In preclinical animal models, initial research has also shown promise in additional substances such as piceatannol and pterostilbene. Furthermore, clinical studies have explored the therapeutic benefits of stilbenes in NDs. Despite promising results in preclinical research, the use of stilbenes in clinical trials is currently limited, with most studies focusing on resveratrol. Although several clinical studies have demonstrated the beneficial impact of resveratrol supplementation on brain health and degenerative consequences, other investigations have yielded ambiguous findings, underscoring the urgent need for more comprehensive and precisely planned clinical research. This study delves into the potential benefits of stilbenes as neuroprotective agents for NDs. It emphasizes the need for more clinical research to enhance our understanding of their therapeutic effectiveness in specific patient groups.

Biomarkers From Discovery to Clinical Application: In Silico Pre-Clinical Validation Approach in the Face of Lung Cancer

Background

Clinical biomarkers, allow better classification of patients according to their disease risk, prognosis, and/or response to treatment. Although affordable omics-based approaches have paved the way for quicker identification of putative biomarkers, validation of biomarkers is necessary for translation of discoveries into clinical application.

Objective

Accordingly, in this study, we emphasize the potential of in silico approaches and have proposed and applied 3 novel sequential in silico pre-clinical validation steps to better identify the biomarkers that are truly desirable for clinical investment.

Design

As protein biomarkers are becoming increasingly important in the clinic alongside other molecular biomarkers and lung cancer is the most common cause of cancer-related deaths, we used protein biomarkers for lung cancer as an illustrative example to apply our in silico pre-clinical validation approach.

Methods

We collected the reported protein biomarkers for 3 cases (lung adenocarcinoma-LUAD, squamous cell carcinoma-LUSC, and unspecified lung cancer) and evaluated whether the protein biomarkers have cancer altering properties (i.e., act as tumor suppressors or oncoproteins and represent cancer hallmarks), are expressed in body fluids, and can be targeted by FDA-approved drugs.

Results

We collected 3008 protein biomarkers for lung cancer, 1189 for LUAD, and 182 for LUSC. Of these protein biomarkers for lung cancer, LUAD, and LUSC, only 28, 25, and 6 protein biomarkers passed the 3 in silico pre-clinical validation steps examined, and of these, only 5 and 2 biomarkers were specific for lung cancer and LUAD, respectively.

Conclusion

In this study, we applied our in silico pre-clinical validation approach the protein biomarkers for lung cancer cases. However, this approach can be applied and adapted to all cancer biomarkers. We believe that this approach will greatly facilitate the transition of cancer biomarkers into the clinical phase and offers great potential for future biomarker research.

Development of a nanozyme-based electrochemical catalyst for real-time biomarker sensing of superoxide and nitric oxide anions released from living cells and exogenous donors

Developing quantitative biosensors of superoxide (O2•-) and nitric oxide (NO) anion is crucial for pathological research. As of today, the main challenge for electrochemical detection is to develop high-selectivity nano-mimetic materials to replace natural enzymes. In this study, the dendritic-like morphological structure of silver organic framework (Ag-MOF) was successfully synthesized via a solvothermal strategy. Owing to the introduction of polymeric composites results in improved electrical conductivity and catalytic activity, which promotes mass transfer and leads to faster electron efficiency. For monitoring the electrochemical signals of O2•- and NO, the Ag-MOF electrode substrate was produced by drop-coating, and composites were designed by cyclic voltammetric potential cycles. The designed electrode substrates demonstrate high sensitivity, wide linear concentrations of 1 nM-1000 μM and 1 nM-850 μM, and low detection limits of 0.27 nM and 0.34 nM (S/N = 3) against O2•- and NO. Aside from that, the sensor successfully monitored the cellular release of O2•-, and NO from HepG2 and RAW 264.7 living cells and has the potential to monitor exogenous NO release from donors of Diethylamine (DEA)-NONOate and sodium nitroprusside (SNP). Additionally, the developed system was applied to the analysis of O2•- and NO in real biological fluid samples, and the results were good satisfactory (94.10-99.57 ± 1.23%). The designed system provides a novel approach to obtaining a good electrochemical biosensor platform that is highly selective, stable, and flexible. Finally, the proposed method provides a quantitative way to follow the dynamic changes in O2•- and NO in biological systems.

Oral and Gingival Crevicular Fluid Biomarkers for Jawbone Turnover Diseases: A Scoping Review

Gingival crevicular fluid (GCF) and oral fluid have emerged as promising diagnostic tools for detecting biomarkers. This review aimed to evaluate the existing literature on using oral fluids as a source of biomarkers for bone turnover diseases affecting the jawbone. A comprehensive search strategy was executed between August 2014 and August 2024 across five major databases (Web of Science, EBSCOhost Dentistry & Oral Sciences Source, Cochrane Library, Scopus, and PubMed) and grey literature sources. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) was applied. The screening was facilitated using Rayyan at rayyan.ai and Endnote X20 software tools, culminating in the evaluation of 14,965 citations from databases and 34 from grey literature. Following rigorous scrutiny, 37 articles were selected for inclusion in this review, encompassing diseases such as periodontitis, medication-related osteonecrosis of the jaw (MRONJ), and osteoporosis. The quality of the included observational studies was assessed using the Revised Risk of Bias Assessment Tool for Non-Randomized Studies (RoBANS 2). Interleukin-1 beta (IL-1β), sclerostin, osteoprotegerin (OPG), and interleukin-34 (IL-34) emerged as significant biomarkers in GCF, and they were mainly from periodontitis and osteoporosis. Osteocalcin (OC), IL-1β, tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), OPG, and matrix metalloproteinase-9 (MMP-9) were significant in oral fluid or saliva, and they were from periodontitis, MRONJ, and osteoporosis. These findings underscore the potential use of oral fluids, which are regarded as non-invasive tools for biomarker identification in bone turnover. Many biomarkers overlap, and it is important to identify other specific biomarkers to enable accurate diagnosis of these conditions.

Role of Exosomes in Salivary Gland Tumors and Technological Advances in Their Assessment

Backgroud: Salivary gland tumors (SGTs) are rare and diverse neoplasms, presenting significant challenges in diagnosis and management due to their rarity and complexity. Exosomes, lipid bilayer vesicles secreted by almost all cell types and present in all body fluids, have emerged as crucial intercellular communication agents. They play multifaceted roles in tumor biology, including modulating the tumor microenvironment, promoting metastasis, and influencing immune responses. Results: This review focuses on the role of exosomes in SGT, hypothesizing that novel diagnostic and therapeutic approaches can be developed by exploring the mechanisms through which exosomes influence tumor occurrence and progression. By understanding these mechanisms, we can leverage exosomes as diagnostic and prognostic biomarkers, and target them for therapeutic interventions. The exploration of exosome-mediated pathways contributing to tumor progression and metastasis could lead to more effective treatments, transforming the management of SGT and improving patient outcomes. Ongoing research aims to elucidate the specific cargo and signaling pathways involved in exosome-mediated tumorigenesis and to develop standardized techniques for exosome-based liquid biopsies in clinical settings. Conclusions: Exosome-based liquid biopsies have shown promise as non-invasive, real-time systemic profiling tools for tumor diagnostics and prognosis, offering significant potential for enhancing patient care through precision and personalized medicine. Methods like fluorescence, electrochemical, colorimetric, and surface plasmon resonance (SPR) biosensors, combined with artificial intelligence, improve exosome analysis, providing rapid, precise, and clinically valid cancer diagnostics for difficult-to-diagnose cancers.

lncRNAs as prognostic markers and therapeutic targets in cuproptosis-mediated cancer

Long non-coding RNAs (lncRNAs) have emerged as crucial regulators in various cellular processes, including cancer progression and stress response. Recent studies have demonstrated that copper accumulation induces a unique form of cell death known as cuproptosis, with lncRNAs playing a key role in regulating cuproptosis-associated pathways. These lncRNAs may trigger cell-specific responses to copper stress, presenting new opportunities as prognostic markers and therapeutic targets. This paper delves into the role of lncRNAs in cuproptosis-mediated cancer, underscoring their potential as biomarkers and targets for innovative therapeutic strategies. A thorough review of scientific literature was conducted, utilizing databases such as PubMed, Google Scholar, and ScienceDirect, with search terms like 'lncRNAs,' 'cuproptosis,' and 'cancer.' Studies were selected based on their relevance to lncRNA regulation of cuproptosis pathways and their implications for cancer prognosis and treatment. The review highlights the significant contribution of lncRNAs in regulating cuproptosis-related genes and pathways, impacting copper metabolism, mitochondrial stress responses, and apoptotic signaling. Specific lncRNAs are potential prognostic markers in breast, lung, liver, ovarian, pancreatic, and gastric cancers. The objective of this article is to explore the role of lncRNAs as potential prognostic markers and therapeutic targets in cancers mediated by cuproptosis.

Predicting Outcomes of Preterm Neonates Post Intraventricular Hemorrhage

Intraventricular hemorrhage (IVH) in preterm neonates presents a high risk for developing posthemorrhagic ventricular dilatation (PHVD), a severe complication that can impact survival and long-term outcomes. Early detection of PHVD before clinical onset is crucial for optimizing therapeutic interventions and providing accurate parental counseling. This study explores the potential of explainable machine learning models based on targeted liquid biopsy proteomics data to predict outcomes in preterm neonates with IVH. In recent years, research has focused on leveraging advanced proteomic technologies and machine learning to improve prediction of neonatal complications, particularly in relation to neurological outcomes. Machine learning (ML) approaches, combined with proteomics, offer a powerful tool to identify biomarkers and predict patient-specific risks. However, challenges remain in integrating large-scale, multiomic datasets and translating these findings into actionable clinical tools. Identifying reliable, disease-specific biomarkers and developing explainable ML models that clinicians can trust and understand are key barriers to widespread clinical adoption. In this prospective longitudinal cohort study, we analyzed 1109 liquid biopsy samples from 99 preterm neonates with IVH, collected at up to six timepoints over 13 years. Various explainable ML techniques-including statistical, regularization, deep learning, decision trees, and Bayesian methods-were employed to predict PHVD development and survival and to discover disease-specific protein biomarkers. Targeted proteomic analyses were conducted using serum and urine samples through a proximity extension assay capable of detecting low-concentration proteins in complex biofluids. The study identified 41 significant independent protein markers in the 1600 calculated ML models that surpassed our rigorous threshold (AUC-ROC of ≥0.7, sensitivity ≥ 0.6, and selectivity ≥ 0.6), alongside gestational age at birth, as predictive of PHVD development and survival. Both known biomarkers, such as neurofilament light chain (NEFL), and novel biomarkers were revealed. These findings underscore the potential of targeted proteomics combined with ML to enhance clinical decision-making and parental counseling, though further validation is required before clinical implementation.

Identification of Plasma Metabolomic Biomarkers of Juvenile Idiopathic Arthritis

Identification of disease and therapeutic biomarkers remains a significant challenge in the early diagnosis and effective treatment of juvenile idiopathic arthritis (JIA). In this study, plasma metabolomic profiling was conducted to identify disease-related metabolic biomarkers associated with JIA. Plasma samples from treatment-naïve JIA patients and non-JIA reference patients underwent global metabolomic profiling across discovery (60 JIA, 60 non-JIA) and replication (49 JIA, 38 non-JIA) cohorts. Univariate analysis identified significant metabolites (q-value ≤ 0.05), followed by enrichment analysis using ChemRICH and metabolic network mapping with MetaMapp and Cytoscape. Receiver operating characteristic (ROC) analysis determined the top discriminating biomarkers based on area under the curve (AUC) values. A total of over 800 metabolites were measured, consisting of 714 known and 155 unknown compounds. In the discovery cohort, 587 metabolites were significantly altered in JIA patients compared with the reference population (q < 0.05). In the replication cohort, 288 metabolites were significantly altered, with 78 overlapping metabolites demonstrating the same directional change in both cohorts. JIA was associated with a notable increase in plasma levels of sphingosine metabolites and fatty acid ethanolamides and decreased plasma levels of sarcosine, iminodiacetate, and the unknown metabolite X-12462. Chemical enrichment analysis identified cycloparaffins in the form of naproxen and its metabolites, unsaturated lysophospholipids, saturated phosphatidylcholines, sphingomyelins, ethanolamines, and saturated ceramides as the top discriminating biochemical clusters. ROC curve analysis identified 11 metabolites classified as highly discriminatory based on an AUC > 0.90, with the top discriminating metabolite being sphinganine-1-phosphate (AUC = 0.98). This study identifies specific metabolic changes in JIA, particularly within sphingosine metabolism, through both discovery and replication cohorts. Plasma metabolomic profiling shows promise in pinpointing JIA-specific biomarkers, differentiating them from those in healthy controls and Crohn's disease, which may improve diagnosis and treatment.

An account of the current status of point-of-care lateral flow tests for kidney biomarker detection

Globally, the primary causes of mortality and morbidity related to kidney ailments can be classified as Acute Kidney Injury (AKI) and Chronic Kidney Disease (CKD). Biomarker detection can have great potential to improve survival, lower mortality, and reduce the cost of treatment of kidney diseases. Considering the chronic nature of CKD, non-invasive identification and monitoring have proven to be useful. Biosensors and more specifically lateral flow test strips (LFTs) are regarded as the most desirable point-of-care instruments which have shown promise in elevating the healthcare industry to a new level. The major aspects of an ideal point-of-care (POC) lateral flow test include its cost effectiveness, high sensitivity and specificity, ease of use, quick result delivery, and quality control. This review provides a detailed account of recent developments, challenges, and opportunities in renal biomarker detection using LFTs including various approaches for sensitivity enhancement along with potential future advancements in POC and LFT kits.

Revolutionary Point-of-Care Wearable Diagnostics for Early Disease Detection and Biomarker Discovery through Intelligent Technologies

Early-stage disease detection, particularly in Point-Of-Care (POC) wearable formats, assumes pivotal role in advancing healthcare services and precision-medicine. Public benefits of early detection extend beyond cost-effectively promoting healthcare outcomes, to also include reducing the risk of comorbid diseases. Technological advancements enabling POC biomarker recognition empower discovery of new markers for various health conditions. Integration of POC wearables for biomarker detection with intelligent frameworks represents ground-breaking innovations enabling automation of operations, conducting advanced large-scale data analysis, generating predictive models, and facilitating remote and guided clinical decision-making. These advancements substantially alleviate socioeconomic burdens, creating a paradigm shift in diagnostics, and revolutionizing medical assessments and technology development. This review explores critical topics and recent progress in development of 1) POC systems and wearable solutions for early disease detection and physiological monitoring, as well as 2) discussing current trends in adoption of smart technologies within clinical settings and in developing biological assays, and ultimately 3) exploring utilities of POC systems and smart platforms for biomarker discovery. Additionally, the review explores technology translation from research labs to broader applications. It also addresses associated risks, biases, and challenges of widespread Artificial Intelligence (AI) integration in diagnostics systems, while systematically outlining potential prospects, current challenges, and opportunities.

Integrating Multi-Organ Imaging-Derived Phenotypes and Genomic Information for Predicting the Occurrence of Common Diseases

As medical imaging technologies advance, these tools are playing a more and more important role in assisting clinical disease diagnosis. The fusion of biomedical imaging and multi-modal information is profound, as it significantly enhances diagnostic precision and comprehensiveness. Integrating multi-organ imaging with genomic information can significantly enhance the accuracy of disease prediction because many diseases involve both environmental and genetic determinants. In the present study, we focused on the fusion of imaging-derived phenotypes (IDPs) and polygenic risk score (PRS) of diseases from different organs including the brain, heart, lung, liver, spleen, pancreas, and kidney for the prediction of the occurrence of nine common diseases, namely atrial fibrillation, heart failure (HF), hypertension, myocardial infarction, asthma, type 2 diabetes, chronic kidney disease, coronary artery disease (CAD), and chronic obstructive pulmonary disease, in the UK Biobank (UKBB) dataset. For each disease, three prediction models were developed utilizing imaging features, genomic data, and a fusion of both, respectively, and their performances were compared. The results indicated that for seven diseases, the model integrating both imaging and genomic data achieved superior predictive performance compared to models that used only imaging features or only genomic data. For instance, the Area Under Curve (AUC) of HF risk prediction was increased from 0.68 ± 0.15 to 0.79 ± 0.12, and the AUC of CAD diagnosis was increased from 0.76 ± 0.05 to 0.81 ± 0.06.

Tick exposure biomarkers: A One Health approach to new tick surveillance tools

The spread of tick-borne disease (TBD) is escalating globally, driven by climate change and socio-economic shifts, underlining the urgency to improve surveillance, diagnostics, and control strategies. Ticks can transmit a range of pathogens increasing the risk of transmission of human and veterinary diseases such as Lyme disease, tick-borne encephalitis, theileriosis, anaplasmosis, or Crimean-Congo hemorrhagic fever. Surveillance methods play a crucial role in monitoring the spread of tick-borne pathogens (TBP). However, there are shortcomings in the current surveillance methods regarding risks related to ticks. Human-tick encounters offer a novel metric for disease risk assessment, integrating human behavior into traditional surveillance models. However, to more reliably measure tick exposure, a molecular marker is needed. The identification of antibodies against arthropod salivary proteins as biomarkers for vector exposure represents a promising avenue for enhancing existing diagnostic and surveillance metrics. Here we explore how the use of tick saliva biomarkers targeting recombinant proteins and synthetic peptides could significantly improve the assessment of TBD transmission risk and the effectiveness of vector control measures. With focused efforts on creating a biomarker against tick exposure suitable for humans and domestic animals alike, tick surveillance, diagnosis and control would be more achievable and aid in reducing the mounting threat of TBP through a One Health lens.

Anti-TCP1 Antibody Is a Potential Biomarker for Diagnosing Systemic Lupus Erythematosus

Systemic lupus erythematosus (SLE) is a chronic inflammatory disease caused by autoantibodies. Serum samples from patients with SLE (n = 10) were compared with those from normal controls (NCs, n = 5) using 21K protein chip analysis to identify a biomarker for SLE, revealing 63 SLE-specific autoantibodies. The anti-chaperonin-containing t-complex polypeptide-1 (TCP1) antibody exhibited higher expression in patients with SLE than in NCs. To validate the specificity of the anti-TCP1 antibody in SLE, dot blot analysis was conducted using sera from patients with SLE (n = 100), rheumatoid arthritis (RA; n = 25), Behçet's disease (BD; n = 28), and systemic sclerosis (SSc; n = 30) and NCs (n = 50). The results confirmed the detection of anti-TCP1 antibodies in 79 of 100 patients with SLE, with substantially elevated expression compared to both NCs and patients with other autoimmune diseases. We performed an enzyme-linked immunosorbent assay to determine the relative amounts of anti-TCP1 antibodies; markedly elevated anti-TCP1 antibody levels were detected in the sera of patients with SLE (50.1 ± 17.3 arbitrary unit (AU), n = 251) compared to those in NCs (33.9 ± 9.3 AU), RA (35 ± 8.7 AU), BD (37.5 ± 11.6 AU), and SSc (43 ± 11.9 AU). These data suggest that the anti-TCP1 antibody is a potential diagnostic biomarker for SLE.

Advances in electrochemical biosensors employing carbon-based electrodes for detection of biomarkers in diabetes mellitus

Background and purpose

The increase in diabetes cases has become a major concern in the healthcare sector, necessitating the development of efficient and minimal diagnostic methods. This study aims to provide a comprehensive examination of electrochemical biosensors for detecting diabetes mellitus biomarkers, with a special focus on the utilization of carbon-based electrodes.

Review approach

A detailed analysis of electrochemical biosensors incorporating various carbon electrodes, including screen-printed carbon electrodes, glassy carbon electrodes, and carbon paste electrodes, is presented. The advantages of carbon-based electrodes in biosensor design are highlighted. The review covers the detection of several key diabetes biomarkers, such as glucose, glycated hemoglobin (HbA1c), glycated human serum albumin (GHSA), insulin, and novel biomarkers.

Key results

Recent developments in electrochemical biosensor technology over the last decade are summarized, emphasizing their potential in clinical applications, particularly in point-of-care settings. The utilization of carbon-based electrodes in biosensors is shown to offer significant advantages, including enhanced sensitivity, selectivity, and cost-effectiveness.

Conclusion

This review underscores the importance of carbon-based electrodes in the design of electrochemical biosensors and raises awareness for the detection of novel biomarkers for more specific and personalized diabetes mellitus cases. The advancements in this field highlight the potential of these biosensors in future clinical applications, especially in point-of-care diagnostics.

Progress and Outlook on Electrochemical Sensing of Lung Cancer Biomarkers

Electrochemical biosensors have emerged as powerful tools for the ultrasensitive detection of lung cancer biomarkers like carcinoembryonic antigen (CEA), neuron-specific enolase (NSE), and alpha fetoprotein (AFP). This review comprehensively discusses the progress and potential of nanocomposite-based electrochemical biosensors for early lung cancer diagnosis and prognosis. By integrating nanomaterials like graphene, metal nanoparticles, and conducting polymers, these sensors have achieved clinically relevant detection limits in the fg/mL to pg/mL range. We highlight the key role of nanomaterial functionalization in enhancing sensitivity, specificity, and antifouling properties. This review also examines challenges related to reproducibility and clinical translation, emphasizing the need for standardization of fabrication protocols and robust validation studies. With the rapid growth in understanding lung cancer biomarkers and innovations in sensor design, nanocomposite electrochemical biosensors hold immense potential for point-of-care lung cancer screening and personalized therapy guidance. Realizing this goal will require strategic collaboration among material scientists, engineers, and clinicians to address technical and practical hurdles. Overall, this work provides valuable insight for developing next-generation smart diagnostic devices to combat the high mortality of lung cancer.

Genetic Markers of Cardiovascular Disease

Cardiovascular Disorders (CVDs) are the leading cause mortality in developed as well as developing nations, and has now emerged as one of the leading causes of disability and mortality around the globe. According to the World Health Organization, four out of every five patients with cardiovascular disease die from a myocardial infarction each year. Numerous genes have been linked to coronary artery disease, influencing mechanisms such as blood pressure regulation, lipid metabolism, inflammation, and cardiac activity. Genetic variations or mutations in these genes can affect lipid metabolism, blood pressure management, and heart function, increasing the risk of obesity, metabolic disorders, and resulting in the development of cardiovascular disease. Understanding the role of genes and related complications are essential for the identification, management, and prevention of cardiovascular conditions. Performing a genetic test for variations in the gene may help identify people as well as their families who are at a greater risk of heart disease, which enables risk identification and timely intervention. . This article investigates the applications of genetic biomarkers in cardiac disorders such as coronary artery disease, hypertension, arrhythmias, cardiomyopathy, and heart failure, with an emphasis on individual genes and their effects on mutation.

Multidrug-resistant tuberculosis

One of predominant contributors to global mortality is tuberculosis (TB), an infection caused by Mycobacterium tuberculosis (MTB). Inappropriate and ineffectual treatment can lead to the development of drug-resistant TB. One of the most common forms of drug-resistant TB is multidrug-resistant tuberculosis (MDR-TB), caused by mutations in the rpoB and katG genes that lead to resistance to anti-TB drugs, rifampicin (RIF) and isoniazid (INH), respectively. Although culturing remains the gold standard, it is not rapid thereby delaying potential treatment and potentially increasing the incidence of MDR-TB. In contrast, molecular techniques provide a highly sensitive and specific alternative. This review discusses the classification of biomarkers used to detect MDR-TB, some of the commonly used anti-TB drugs, and DNA mutations in MTB that lead to anti-TB resistance. The objective of this review is to increase awareness of the need for rapid and precise detection of MDR-TB cases to decrease morbidity and mortality of this infectious disease worldwide.

Integrative Assessment of Seminal Plasma Biomarkers: A Narrative Review Bridging the Gap between Infertility Research and Clinical Practice

Infertility represents a significant global health challenge impacting millions of couples worldwide. Approximately half of all infertile couples exhibit compromised semen quality, indicative of diminished male fertility. While the diagnosis of male infertility traditionally relies on semen analysis, its limitations in providing a comprehensive assessment of male reproductive health have spurred efforts to identify novel biomarkers. Seminal plasma, a complex fluid containing proteins, lipids, and metabolites, has emerged as a rich source of such indicators. Reproduction depends heavily on seminal plasma, the primary transporter of chemicals from male reproductive glands. It provides a non-invasive sample for urogenital diagnostics and has demonstrated potential in the identification of biomarkers linked to illnesses of the male reproductive system. The abundance of seminal proteins has enabled a deeper understanding of their biological functions, origins, and differential expression in various conditions associated with male infertility, including azoospermia, asthenozoospermia, oligozoospermia, teratozoospermia, among others. The true prevalence of male infertility is understated due to the limitations of the current diagnostic techniques. This review critically evaluates the current landscape of seminal plasma biomarkers and their utility in assessing male infertility. Βy bridging the gap between research and clinical practice, the integrative assessment of seminal plasma biomarkers offers a multimodal approach to comprehensively evaluate male infertility.